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|Title:||Tool wear forecast using Dominant Feature Identification of acoustic emissions||Authors:||Pang, C.K.
Least square error (LSE)
Principal component analysis (PCA)
Principal feature analysis (PFA)
Singular value decomposition (SVD)
Tool condition monitoring (TCM)
|Issue Date:||2010||Citation:||Pang, C.K., Zhou, J.-H., Zhong, Z.-W., Lewis, F.L. (2010). Tool wear forecast using Dominant Feature Identification of acoustic emissions. Proceedings of the IEEE International Conference on Control Applications : 1063-1068. ScholarBank@NUS Repository. https://doi.org/10.1109/CCA.2010.5611259||Abstract:||Identification and online prediction of lifetime of cutting tools using cheap sensors is crucial to reduce production costs and down-time in industrial machines. In this paper, we use the acoustic emission from an embedded sensor for computation of features and prediction of tool wear. A reduced feature subset which is optimal in both estimation and clustering least square errors is then selected using a new Dominant Feature Identification (DFI) algorithm to reduce signal processing and number of sensors required. Tool wear is then predicted using an ARMAX model based on the reduced features. Our experimental results on a ball nose cutter in a high speed milling machine show a reduction in 16.83% in mean relative error when compared to other methods proposed in the literature. © 2010 IEEE.||Source Title:||Proceedings of the IEEE International Conference on Control Applications||URI:||http://scholarbank.nus.edu.sg/handle/10635/72037||ISBN:||9781424453627||DOI:||10.1109/CCA.2010.5611259|
|Appears in Collections:||Staff Publications|
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